NLP AI Engineer

• Design, fine-tune, and optimize Large Language Models using techniques such as

.

• Architect scalable distributed training pipelines using modern deep learning frameworks and GPU clusters.

• Develop high-quality datasets, synthetic data generation pipelines, and evaluation frameworks to improve model accuracy, robustness, and reliability.

• Optimize large-scale GPU training, inference performance, experiment tracking, and model serving.

• Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding models, vector search, and agentic AI workflows.

• Develop automated benchmarking, safety testing, hallucination detection, and Responsible AI evaluation frameworks.

• Collaborate with AI researchers, software engineers, data scientists, and product teams to deliver enterprise AI applications.

• Lead architecture reviews, establish best practices for LLM development, and mentor junior AI engineers.

• Evaluate emerging NLP research, foundation models, and AI frameworks to drive continuous innovation.

• Ensure AI solutions meet enterprise requirements for scalability, security, compliance, and operational excellence.

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related technical discipline (or equivalent professional experience).

•

in Artificial Intelligence, Machine Learning, NLP, or LLM engineering.

• Expert-level programming skills in

and transformer-based architectures.

• Proven experience fine-tuning and deploying

for production environments.

• Strong expertise in distributed training technologies, including

.

• Hands-on experience with

, or other preference optimization techniques.

• Experience with cloud platforms such as

for AI workloads.

• Strong understanding of machine learning algorithms, deep learning, NLP, model evaluation, and MLOps practices.

• Excellent analytical, communication, collaboration, and technical leadership skills.

• Publications in leading AI and Machine Learning conferences such as

.

• Experience with

, vision-language models (VLMs), speech models, or foundation models.

• Knowledge of

.

• Experience with synthetic data generation, Responsible AI, AI governance, fairness, and model safety.

• Contributions to open-source LLM training frameworks, AI research, patents, or technical publications.

• Experience deploying AI applications using Kubernetes, Docker, Ray, and enterprise MLOps platforms.